How Shunya Outpaces NVIDIA Riva for Production Speech-to-Text

NVIDIA Riva is built for teams that want to run speech AI on their own infrastructure. It combines GPU-accelerated ASR and TTS with configurable models, streaming and offline APIs, and deployment options across cloud, on-premise and edge environments.

That makes Riva a powerful option for organizations that already operate NVIDIA infrastructure and want deep control over speech inference.

But building production speech applications involves more than having a fast ASR engine.

You need language coverage, domain specialization, real-time performance, terminology handling, speaker diarization and speech intelligence. You also need to decide how much of the infrastructure and model stack your team wants to own.

That’s where Shunya Zero STT takes a different approach.

Zero STT is built for production speech workloads with 216+ languages, 55+ Indian languages and sub-500ms first-token positioning, speaker diarization and speech intelligence, with dedicated models for Indic, code-switched and medical speech.

The question isn’t simply which platform is faster.

It’s:

Which speech-to-text platform gives you the right balance of performance, language coverage, specialization and ease of deployment for your production workload?

Shunya and NVIDIA Riva: at a glance

Riva and Shunya both support production speech workloads, but they take different approaches to deployment and speech infrastructure.

Aspect	Shunya Zero STT	NVIDIA Riva
Platform	Enterprise speech-to-text platform	GPU-accelerated speech AI SDK
Deployment	Cloud and enterprise private deployment options	Cloud, on-premise and edge
Pricing	From $0.0039/min for Zero STT	Free developer access within limits; paid Riva Enterprise
Key strength	Multilingual speech, Indic depth and speech intelligence	GPU acceleration, performance and infrastructure control
Languages	216+	Model dependent
Indian languages	55+	Multiple models support Hindi; coverage varies by model
Real-time	Sub-500ms first-token positioning	Real-time streaming
Offline	Yes	Yes
Speaker diarization	Supported	Supported on selected models
Custom vocabulary	Supported	Word boosting
Code-switching	Dedicated model	Model dependent
Speech intelligence	Intent, sentiment, emotion and more	Build through Riva/NVIDIA pipeline components
Medical ASR	Zero STT Med	Custom model/pipeline
Best fit	Enterprise speech and multilingual voice applications	GPU-based private speech infrastructure

NVIDIA’s current Riva documentation provides both streaming and offline ASR deployments and a model matrix showing language, inference mode and capabilities by model.

Riva is built for infrastructure control

Riva’s biggest advantage is NVIDIA’s infrastructure stack.

It is designed for teams that want to deploy speech AI where they control the compute.

That can mean:

• Cloud GPU infrastructure
• On-premise servers
• Edge devices
• Private environments
• High-throughput deployments

NVIDIA says Riva Enterprise can scale to hundreds of thousands of concurrent users across cloud, on-premise and edge environments, with real-time performance below 300 milliseconds using NVIDIA TensorRT optimizations.

Riva also provides Python and C++ clients, streaming and offline APIs, and model customization capabilities.

Shunya’s model is different.

Instead of giving teams a speech SDK that they assemble around their infrastructure, Zero STT is delivered as a production speech platform.

That shifts the emphasis from:

How do we run speech inference?

to:

How do we integrate speech into the product?

Which is more accurate?

Accuracy depends heavily on the model and workload.

Riva isn’t one ASR model. It is a platform that can expose different ASR models, including Parakeet and Canary variants. NVIDIA’s current model catalog includes multilingual models with different language and capability coverage.

For example, NVIDIA’s Canary-1B model supports 11 recognition languages, including Hindi, Japanese and Korean, while a Riva Parakeet multilingual model supports 25 languages.

Shunya currently publishes a 3.10% composite WER in English across eight OpenASR benchmarks for Zero STT.

Benchmark	Shunya Zero STT
LibriSpeech Clean	0.71%
SPGISpeech	1.10%
TED-LIUM	1.43%
LibriSpeech Other	2.17%
AMI	4.19%
VoxPopuli	4.34%
GigaSpeech	4.99%
Earnings22	5.83%
Composite	3.10%

These are published Shunya benchmark results.

They aren’t a universal prediction of performance on your data.

The best comparison is still your own audio.

The feature difference is really a difference in abstraction

Riva gives you the building blocks for a speech system.

Shunya gives you more of the speech system itself.

Feature	Shunya	NVIDIA Riva
Batch transcription	✓	✓
Real-time streaming	✓	✓
Speaker diarization	✓	✓ on supported models
Automatic punctuation	✓	✓
Word timestamps	✓	✓
Confidence scores	✓	✓
Language identification	✓	Model dependent
Word boosting	✓	✓
Custom terminology	✓	✓
Code-switching	Dedicated model	Model dependent
Indian-language specialization	Dedicated Indic model	Model dependent
Sentiment	Speech intelligence	Additional pipeline
Emotion	Speech intelligence	Additional pipeline
Intent	Speech intelligence	Additional pipeline
Medical model	Zero STT Med	Custom model/pipeline
PII	Supported	Additional pipeline
Private deployment	Enterprise options	Core deployment model
Edge	Enterprise options	Core deployment model

NVIDIA’s current ASR customization documentation shows support for word boosting, VAD, profanity filtering and speaker diarization, with availability varying by model.

That variability is important.

With Riva, model selection becomes part of system design.

Real-time performance: both are built for it

Unlike a batch-oriented ASR system, Riva is designed from the ground up for real-time inference.

NVIDIA documents streaming ASR through Riva clients and positions Riva Enterprise for sub-300ms real-time performance with NVIDIA optimizations.

Shunya publicly positions Zero STT around sub-500ms first-token latency and reports 240+ concurrent streams per GPU.

So the interesting comparison isn’t simply:

real-time: yes / no

It is:

first-result latency → final latency → concurrency → GPU cost → engineering complexity

For teams running NVIDIA hardware at scale, Riva’s optimization can be extremely attractive.

For teams that want a managed speech service without building their own GPU serving layer, Shunya can provide a simpler path.

Indian languages are where specialization matters

NVIDIA has multilingual ASR models that include Indian languages.

For example, NVIDIA’s Canary-1B model explicitly supports Hindi, while its Riva model catalog includes a dedicated Hindi Conformer ASR model.

Shunya takes a broader Indic-first approach.

Zero STT Indic currently supports 55+ Indian languages, while Zero STT Codeswitch is designed for mixed-language speech.

This matters when your application needs:

• Indian languages
• Regional speech
• Hinglish
• Tanglish
• Low-resource languages
• Transliteration
• Domain-specific terminology

Language support should therefore be evaluated at the level of your actual language mix, not simply by looking at the number of languages listed on a product page.

Code-switching is a different problem from multilingual ASR

Consider:

“Mera account block ho gaya, can you help me reactivate it?”

The speaker isn’t selecting Hindi from a dropdown and then English.

They’re speaking naturally.

Shunya’s Zero STT Codeswitch model is designed specifically for mixed-language speech such as Hinglish and Tanglish.

For customer-facing applications, this matters because transcription feeds other systems.

speech → transcript → intent → action

If the transcript changes, the downstream result can change too.

Riva supports multilingual ASR through different models, but its language coverage and capabilities are model-dependent.

For applications with heavy code-switching, a dedicated model can therefore be more relevant than general multilingual support.

Riva’s strongest advantage: GPU acceleration

This is where NVIDIA might have a strength.

Riva is designed specifically around NVIDIA GPUs and accelerated speech inference.

NVIDIA provides TensorRT-based optimizations and lets organisations deploy speech AI close to where their applications run.

That makes Riva attractive when you already have:

NVIDIA GPUs

Kubernetes infrastructure

ML platform teams

Private data centres

Edge compute

and want to maximize infrastructure utilization.

Shunya’s advantage is different.

You don’t need to turn speech recognition into an infrastructure project before your application can use it.

For many teams, that difference matters more than raw inference throughput.

Customization: Riva gives you deep control

Riva provides a range of customization options.

NVIDIA’s current documentation supports:

• Word boosting
• Voice activity detection
• Profanity filtering
• Speaker diarization
• Model-specific customization
• Streaming and offline APIs

NVIDIA also provides model families that can be deployed and customized for different language and inference requirements.

This makes Riva particularly attractive when your team wants to build a custom speech pipeline.

Shunya’s approach is more product-oriented.

Instead of asking teams to assemble models and pipeline components for every use case, it provides specialized speech models such as:

Zero STT Universal

Zero STT Indic

Zero STT Codeswitch

Zero STT Med

The trade-off is straightforward:

Riva gives you more control over the infrastructure and model pipeline.

Shunya gives you more of the production speech capability out of the box.

The hidden cost of running your own Riva infrastructure

Riva itself is available to NVIDIA Developer Program members at no charge within the applicable usage limits. Riva Enterprise is NVIDIA’s paid subscription for production deployments beyond those limits and includes enterprise support and long-term version support.

But running a Riva deployment still means owning infrastructure.

You may need:

NVIDIA GPUs

Riva is designed around NVIDIA GPU acceleration.

Container infrastructure

Riva deployment uses NVIDIA containers and the surrounding infrastructure required to serve them.

Capacity planning

You need enough GPU capacity for peak traffic.

Scaling

You need to manage replicas, routing and concurrency.

Monitoring

You need visibility into GPU utilization, latency and service health.

Model management

You choose which model to deploy and when to update it.

NVIDIA’s own deployment documentation shows model selection based on language, inference mode, capability and GPU memory requirements.

That level of control is valuable.

It is also operational responsibility.

Pricing: Riva vs Shunya

Shunya currently lists Zero STT from $0.0039/min for batch transcription, with separate prices for specialized models.

Riva’s economics work differently.

The developer version can be used within NVIDIA’s stated free limits, while Riva Enterprise is a paid subscription licensed for production use and available in one- or three-year terms. NVIDIA says Enterprise subscriptions include unlimited usage on cloud and on-premise, subject to the subscription terms, along with enterprise support.

That means the comparison isn’t simply:

Riva price vs Shunya price

You need to compare:

GPU infrastructure + Riva licensing + utilization + operations

against:

managed transcription usage + enterprise requirements

For organizations that already own NVIDIA infrastructure, Riva can be extremely compelling.

For organizations that don’t, the infrastructure becomes a much larger part of the decision.

Riva can be the better choice when…

NVIDIA Riva can be the better fit when:

You already operate NVIDIA infrastructure

Your team has GPU clusters and the expertise to run them.

You need deep control

You want to select models, configure pipelines and control deployment.

Edge deployment matters

Your application needs speech inference close to the device.

You need very high concurrency

You have the infrastructure to scale GPU-backed inference.

Your speech stack is part of your ML platform

You want speech to run alongside your own models and services.

Riva’s ability to deploy across cloud, on-premise and edge environments is a major strength.

When Shunya makes more sense

Shunya becomes more compelling when:

You need broad multilingual speech

Zero STT currently supports 216+ languages.

Indian languages are central

You need 55+ Indian languages through a dedicated Indic model.

Code-switching is common

Your users switch between Indian languages and English.

You need specialized speech

Medical and other domain-specific speech workloads need dedicated model paths.

You need speech intelligence

You want intent, sentiment, emotion and speaker information alongside transcription.

You want to focus on the product

You’d rather integrate speech than operate the GPU-backed speech infrastructure yourself.

Shunya and NVIDIA Riva by use case

Use case	Better fit	Why
NVIDIA GPU infrastructure already exists	Riva	Built for NVIDIA acceleration
Custom speech pipeline	Riva	Deep model and pipeline control
Edge speech AI	Riva	Strong edge deployment story
Private speech infrastructure	Both	Different deployment approaches
General production ASR API	Shunya	Managed speech platform
Indian-language application	Shunya	55+ Indic languages
Hinglish application	Shunya	Dedicated codeswitching model
Speech intelligence	Shunya	Intent, sentiment, emotion and related capabilities
Medical speech	Shunya	Dedicated Zero STT Med
GPU-optimized enterprise inference	Riva	NVIDIA acceleration
Fast API integration	Shunya	Managed service
Highly customized ASR infrastructure	Riva	Infrastructure and model control

Can you use Riva and Shunya together?

Yes.

Different workloads can use different speech backends.

For example:

NVIDIA Riva

→ edge applications
→ existing GPU infrastructure
→ custom inference pipelines

Shunya

→ multilingual production APIs
→ Indian-language workloads
→ code-switching
→ speech intelligence
→ specialized enterprise speech

This can be useful when a company wants direct control over some inference workloads while using managed speech infrastructure for others.

How should you evaluate them?

Don’t choose based only on GPU throughput, WER or language count.

Use representative production audio.

Test:

Your languages

Your accents

Your terminology

Your recording conditions

Your number of speakers

Your concurrency

Then compare:

Metric	What to measure
WER	Overall transcription
Entity accuracy	Names, products and terminology
Number accuracy	Dates, amounts and identifiers
Code-switch accuracy	Mixed-language speech
Diarization	Speaker attribution
First-result latency	Responsiveness
Final latency	End-to-end performance
Concurrent streams	Production scale
GPU utilization	Infrastructure efficiency
Cost per hour	Total speech cost
Engineering effort	Operational burden

For Riva, also include the cost of the GPU infrastructure and the engineering team responsible for operating it.

For Shunya, measure the actual API cost and the deployment options required by your organization.

The best ASR platform is the one that performs well on your audio at the economics you can actually sustain.

Final words

NVIDIA Riva is a powerful speech AI SDK for teams that want control over their speech infrastructure.

It provides GPU-accelerated ASR, streaming and offline inference, model customization and deployment across cloud, on-premise and edge environments.

For organizations already operating NVIDIA infrastructure, that can be a major advantage.

Shunya takes a different approach.

Zero STT combines 216+ languages, 55+ Indian languages, sub-500ms first-token positioning, 240+ concurrent streams per GPU and speech intelligence, alongside dedicated models for Indic, code-switched and medical speech.

The difference is not simply about performance.

It’s about where you want the complexity to live.

Riva puts more control in your infrastructure.

Shunya puts more production speech capability behind the API.

For teams with GPU infrastructure and a strong ML platform, Riva can be the right choice.

For teams building multilingual enterprise speech applications, especially those involving Indian languages, code-switching and speech intelligence, Shunya can be the better fit.

The right question isn’t:

“Which ASR engine is faster?”

It’s:

“Which speech platform gives us the accuracy, language coverage, control and production simplicity our application actually needs?”

Frequently asked questions

Is Shunya better than NVIDIA Riva?

It depends on your requirements. Riva is particularly strong for teams that want GPU-accelerated speech AI and deep control over infrastructure and model pipelines. Shunya is particularly differentiated around multilingual enterprise speech, Indian languages, code-switching and speech intelligence.

What is NVIDIA Riva?

NVIDIA Riva is a GPU-accelerated speech AI SDK for automatic speech recognition and text-to-speech. It provides streaming and offline APIs and can be deployed across cloud, on-premise and edge environments.

How many languages does NVIDIA Riva support?

Coverage depends on the Riva model. NVIDIA’s current catalog includes multilingual models such as Canary and Parakeet, with different language sets. For example, Canary-1B supports 11 recognition languages, while the Parakeet multilingual model listed by NVIDIA supports 25 languages.

How many languages does Shunya support?

Shunya currently markets Zero STT across 216+ languages, with 55+ Indian languages supported through Zero STT Indic.

Does NVIDIA Riva support Indian languages?

Yes. NVIDIA currently provides Riva ASR models that include Hindi and other languages, although coverage depends on the specific model.

Does Shunya support Indian languages?

Yes. Zero STT Indic supports 55+ Indian languages.

Does Riva support speaker diarization?

Yes, but support depends on the specific Riva model. NVIDIA’s current customization documentation lists speaker diarization for several supported ASR models.

Does Shunya support speaker diarization?

Yes. Speaker diarization is part of Zero STT’s published capabilities.

Does NVIDIA Riva support real-time transcription?

Yes. Riva provides streaming ASR APIs, and NVIDIA positions Riva Enterprise for real-time performance below 300 milliseconds using its acceleration stack.

Does Shunya support real-time transcription?

Yes. Shunya provides streaming recognition and publicly positions Zero STT around sub-500ms first-token latency.

Does Riva support custom vocabulary?

Yes. NVIDIA documents word boosting as a supported ASR customization for multiple Riva models.

Does Shunya support code-switching?

Yes. Shunya offers a dedicated Zero STT Codeswitch model for mixed-language speech such as Hinglish and Tanglish.

Which is better for Indian voice applications?

Shunya can be the stronger fit when Indian languages and mixed-language speech are central because it provides a dedicated Indic model covering 55+ Indian languages and a dedicated code-switching model.

Which is better for GPU-based private deployments?

Riva is particularly well suited to GPU-based private deployments because it is designed around NVIDIA acceleration and supports cloud, on-premise and edge environments.

How much does NVIDIA Riva cost?

NVIDIA provides Riva through its developer program within stated usage limits, while Riva Enterprise is a paid subscription for production deployments beyond those limits. NVIDIA states that Riva Enterprise subscriptions are available on one- or three-year terms.

How much does Shunya STT cost?

Shunya currently lists Zero STT from $0.0039/min for batch transcription, with separate pricing for specialized model variants.

Which is better for medical speech?

Shunya offers a dedicated Zero STT Med model. Riva can support domain-specific customization, but medical workflows generally require assembling and customizing the appropriate model pipeline.

Can I use Riva and Shunya together?

Yes. Different workloads can use Riva or Shunya depending on deployment, infrastructure, language, customization and latency requirements.